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About CynLr Just like a baby’s brain, CynLr Visual Intelligence stack makes Robots to instinctively see & pick any object under any ambience, without any training . ( a demo video link ). Today, we don’t have a robot that can fit a screw into a nut without slipping a thread. Imagine what it would take for a robot to assemble a Smartphone or a car by putting together 1000s of parts with varied shapes and weights, all in random orientations. Thus factories become complex, needing heavy customization of their environment. CynLr enabled visual robots intuitively handles any object, even from a clutter – a universal alternative to custom machines, simplifying factory lines into modular LEGO blocks of micro-factories. Simplifying factories with robots that can pick & place any object has been a 40 year old pipe dream - touted as The Holy Grail of Robotics . As a GPU developer, you will be responsible for building and translating the entire Vision Algorithm & Learning SW Stack into a performance-optimized code block and build mathematical models that are better represented in GPU. Requirements in Practice : Experienced with Low-level CUDA API Strong with fundamentals of C++/C. Adept with Visual Studio developer toolchain. Experience in low-level performance analysis and optimization with a strong understanding of the GPU HW architecture and HW-oriented performance optimization. including proficiency using GPU profiling tools such as NVIDIA Visual Profiler, NVIDIA Nsight Compute and Graphics Developer tools for debugging Optimizing Time Continuous kernels - not just High-Level Kernel optimizations that are shipped with CUDA. Design the framework of Pipelined Image processing CUDA core optimization to achieve maximum performance for a pipelined processing between multiple blocks of functions executing simultaneously. Dynamic Load balancing between kernels and functions. Interleaving processing between CPU and GPU and runtime modification of GPU processing control flow from CPU. Practice with NvidiaDirect to access memory directly from Peripheral devices (PCIe), Display and USB, bypassing the CPU Practice with constructing Direct Visualization of GPU Memory for Debugging without CPU transfer Experienced with designing and optimising foundational neural networks and modelling neurons (basically optimizing mathematical models that involve time-weighted kernels) ground up. Exposure to Omniverse is a Plus Must have an understanding of : GPU-based application development. Knowledge of CUDA (Excellency is not necessary) State machine architecture Realtime computing Memory architectures and optimizations. MIMD, SIMD Good to have experience and practice with Compiler working and construction. CPU architectures – x86, x64 & ARM Hardware-associated driver development. OS and layers (Board Support Packages, BIOS, UEFI, BootLoader) UI-based deployable application development Team Structure: The engineering team will comprise of – Algo Team, GPU Team, SW Dev Team & HW Team. Members of other teams will be passive members of each team apart from the team they lead. The Algo Team will provide the Neural Models & Vision algorithms, while the GPU Team will provide the GPU optimizations for the algos, HW team will provide the HW integration and SW team with translate GPU optimized algos into SW blocks. Each team will split the implementation among other teams and guide them through the implementation. Every team member will be a passive member of all other teams. What will you do? Simplistically put – you will think all the algorithms that the Neuroscience team comes up with through GPU for maximum performance. You will break down the entire pipeline of processing that imitates the visual pathway into optimized blocks and kernels of processing in GPU. You will meticulously discover the mathematical models that gives the maximum timing performance for every Neural Model/algorithm that the Vision and Neuro team comes up with. You will also be building some aspects of Debugging, profiling and Image visualizing tools for GPU. How will you Do? You have complete freedom here, but you will be subjected to reviews. Since this is a startup and the product is not yet well-defined, you would be the one with the responsibility of defining it. Expect things to be not orderly and requirements to not be solid. Part of your design effort largely involves requirements building too and developing architectures that are agnostic to such requirement changes. The SW part of the product significantly evolves as per your thought process and will henceforth carry your signature in it. You will also be building a team as the product evolves to maintain and develop further. Though confined to a focused area, the work is pretty much expected to be entrepreneurial with the exact advantages and difficulties of a startup.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.